Model Registry

A Model Registry is a centralized system designed to manage and store machine learning models and their associated metadata. It serves as a repository where models can be registered, versioned, and tracked throughout their lifecycle. By maintaining a Model Registry, data scientists and machine learning engineers can efficiently manage different model versions, ensuring reproducibility and facilitating collaboration. Additionally, it allows for the organization of model artifacts, linking them to experiments and providing insights into the data used for training. This structured approach enhances the overall workflow in machine learning operations, making it easier to deploy and monitor models effectively.

ML model registry — the “interface” that binds model experiments and model deployment

Towards Data Science

ML model registry — the “interface” that binds model experiments and model deployment. MLOps in Practice — A deep- dive into ML model registries, model versioning and model lifecycle management..

Register and Deploy Models with SageMaker Model Registry

Towards Data Science

An Introduction To SageMaker Model Registry Continue reading on Towards Data Science

MLOps in a Nutshell: Model Registry, ML Metadata Store and Model Pipeline

Python in Plain English

The following is a collection of three shorter-form content pieces I’ve published on LinkedIn. They present three core MLOps (Machine Learning Operations) concepts in a concise manner: * Model Registr...

Models

Django documentation

Model API reference. For introductory material, see Models . Model field reference Field attribute reference Model index reference Constraints reference Model _meta API Related objects reference Model...

Advent of 2022, Day 14 – Registering the models

R-bloggers

In the series of Azure Machine Learning posts: Important asset is the “Models” in navigation bar. This feature allows you to work with different model types -__ custom, MLflow, and Triton. What you do...

A Catalog of Models

Towards Data Science

There are many types of models--deterministic, empirical, probabilistic. You need to understand which type is best for your application.

Models, MLFlow, and Microsoft Fabric

Towards Data Science

Fabric Madness part 5 Image by author and ChatGPT. “Design an illustration, with imagery representing multiple machine learning models, focusing on basketball data” prompt. ChatGPT, 4, OpenAI, 25th A...

Build a Personal ML Model Registry with Replicate in 5 mins

Towards AI

Developer’s Guide to Hosting any ML Model and Charging for It Continue reading on Towards AI

Model Garden overview

TensorFlow Guide

The machine learning models in the Model Garden include full code so you can test, train, or re-train them for research and experimentation. The Model Garden includes two primary categories of models:...

Model Garden overview

TensorFlow Guide

AI/ML Model Validation Framework

Towards Data Science

Model Risk Management (MRM) is a standard practice for any financial institution to assess the model risk. However, in the analytics space, there is a paradigm shift from earlier mainstream…

The Data Mesh Registry — a Window into Your Data Mesh

Towards Data Science

The Data Mesh Registry — The Window into Your Data Mesh Traditional data catalogs have been built when there was no simple way to search and find data in a sprawling data landscape. Metadata is moved ...